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Research Foresight: Priority-Setting in Science

Ben R. Martin, +1 more
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The article was published on 1989-11-01 and is currently open access. It has received 138 citations till now. The article focuses on the topics: Futures studies.

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What is research collaboration

TL;DR: In this paper, the authors distinguish between collaboration at different levels and show that inter-institutional and international collaboration need not necessarily involve inter-individual collaboration, and argue for a more symmetrical approach in comparing the costs of collaboration with the undoubted benefits when considering policies towards research collaboration.
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Three frames for innovation policy: R&D, systems of innovation and transformative change

TL;DR: In this paper, the authors identified two established frames as coexisting and dominant in contemporary innovation policy discussions and argued that all three frames are relevant for policymaking, but exploring options for transformative innovation policy should be a priority.
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What makes basic research economically useful

TL;DR: In this article, economic analysis has helped us understand the strong economic dimension in the explosive growth of science, and (more recently) the reasons for continuing public subsidies, but the growing domination of the "market failure" approach has led to the analytical neglect of two major questions for policy-makers.
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Technology foresight for wiring up the national innovation system: experiences in Britain, Australia and New Zealand

TL;DR: In this paper, the authors analyze and compare the approaches to foresight in these countries, identifying the strengths and weaknesses of each approach, and propose a new rationale for technology foresight, which centers on its role in "wiring up" and thereby strengthening the national innovation system.
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On the Future of Technological Forecasting

TL;DR: Technological forecasting is now poised to respond to the emerging needs of private and public sector organizations in the highly competitive global environment as mentioned in this paper, and new tools are anticipated, borrowing variously from fields such as political science, computer science, scientometrics, innovation management, and complexity science.